Image sensor systems often acquire a plurality of data captures (frames) at regular temporal intervals over a period of time. However, transient blockages (e.g., a helicopter rotor, landing gear, a person or object passing in front of the camera, pixelation from interference, or other such blockages or corruption artifacts), whether periodic or aperiodic, can intrude on one or more frames. Such transient blockage of images can, for example, disrupt continuity of an image feed (e.g., video or time lapse), result in false or missed detections in threat detection systems, or otherwise corrupt at least a portion of the captured image data. Nevertheless, conventionally, the frames including the transient blockage(s) are either used as-is or entirely discarded. However, discarding the blocked/corrupted frames introduces temporal discontinuities to the image feed, such as, for example, an unnatural “freezing” of the image content during the dropped frame or a visually perceptible blank frame displayed between two frames having image content, Discarding the blocked/corrupted frames also represents a total loss of data from the discarded frames.
Another conventional technique sets a detected intensity threshold to recognize a transient blockage. If the intensity threshold is exceeded, the conventional technique “erodes” the recognized blocked portion of the image by iteratively widening the estimated blockage size within the image until the detected intensity value matches the background intensity. The eroded region of the image is then discounted. However, this technique is highly processor intensive, time consuming, and must be performed individually for each optical sensor used in the threat detection system. Thus, this conventional technique does not deliver rapid results, especially for a size or weight limited host platform such as an aircraft or other vehicle. Additionally, because data from an individual sensor's field of view (FOV) is often at least partially corrupted by other factors (e.g., inclusion of the sun within the FOV), the “erosion” process can be negatively impacted by non-blockage high intensity portions of the image, rendering the erosion ineffective or detrimental to image quality.